Chen Xue, Li Xiaohui, Yang Sibo, Yu Xin, Liu Aichun
Department of Hematology, Harbin Medical University Cancer Hospital, Harbin 150081, China.
Contributed equally as co-first authors.
Biomed Opt Express. 2018 Feb 7;9(3):1057-1068. doi: 10.1364/BOE.9.001057. eCollection 2018 Mar 1.
Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies.
淋巴瘤是一种影响人体淋巴和造血系统的重大癌症。在这项工作中,介绍了使用激光诱导击穿光谱法(LIBS)对全血样本进行淋巴瘤鉴别。从淋巴瘤患者和健康对照者采集的全血样本被滴加到标准定量滤纸上,并用1064 nm调Q Nd:YAG激光进行烧蚀。选择钙(Ca)、铁(Fe)、镁(Mg)、钾(K)和钠(Na)的16条原子和离子发射线来鉴别癌症疾病。化学计量学方法,包括主成分分析(PCA)、线性判别分析(LDA)分类和k近邻(kNN)分类,用于建立鉴别模型。LDA和kNN模型对淋巴瘤均取得了非常好的鉴别性能,准确率超过99.7%,灵敏度超过0.996,特异性超过0.997。这些结果表明,基于全血的LIBS技术与化学计量学方法相结合,可以作为一种快速、微创且准确的方法用于检测和鉴别人类恶性肿瘤。